Signals, Instruments, and Systems – W11 Field Instruments ... · Motivation for Sensor Networks...

Signals, Instruments, and Systems – W11Field Instruments and Sensor Networks for

Environmental Monitoring –Energy Management,

Operation Principles, and Deployment

1

Outline• Power management in field

instruments– Power consumption– Power generation– Power storage

• Sensor networks for environmental monitoring: – Basic principles– The Sensorscope project– Mica-Z – a desktop sensor node

2

Power Management in Field Instruments

3

Motivation

• Energy is one of the key bottlenecks for autonomous/unattended operation of embedded systems

• Field instruments as extreme examples– Power consumption– Power generation – Power storage

Power Consumption

5

PowerP U I= ⋅

Examples:• MICAz:

2 * 1,5V battery, 25 mA power consumption → 2*1,5V*0,025A=80mW(standby: 80 μW)

• Campell Scientific:3D ultrasonic

anemometer:1,2W or 2,4W

• SHT1x temperature and humidity sensor:2μW – 3mW

6

EnergyE P t= ⋅

Examples:• Rechargeable battery (NiMH):

1,2V*2000mAh=2400mWh=2,4Wh

• Rechargeable battery (LiPo): 3,7V*1340mAh=4958mWh=4,958Wh

• Communication and computation energy:(remember 1 Ws=1 Joule)

1999 (Bluetooth

Technology)2004

(150nJ/bit) (5nJ/bit)1.5mW* 50uW

~ 190 MOPS(5pJ/OP)

Computation

Communication

7

Managing Power ConsumptionConsumption vs. capabilities (example 1):

• Disdrometer 1 (“tipping bucket”) (≈ 0W)- no snow, sleet- no robust to freezing- no drop statistics- resolution

• Disdrometer 2 (laser) (≈ 10W)+ snow, sleet+ robust to freezing+ drop statistics- expensive- delicate

• Disdrometer 3 (hot plate) (≈ 100W)+ snow, sleet+ robust freezing+ simple- drop statistics

8

Managing Power ConsumptionConsumption vs. capabilities (example 2):

• Anemometer 1 (cup) (≈ 0W)- 1D (main direction)- no robust to freezing- temporal resolution (0.3 Hz)- minimal wind speed high

• Anemometer 2 (ultrasonic) (≈ 10W)+ 2D+ some snow, sleet+ some robustness to freezing+ temporal resolution

(up to 60Hz)- expensive

• Anemometer 3 (Anemometer 2+heater) (≈ 100W)+ 2D+ snow, sleet+ robust to freezing+ temporal resolution- expensive 9

Managing Power ConsumptionConsumption vs. processing speed:

• P ~ fclock• Energy/operation = const

Consumption vs. transmission power:

• P=f(PRF)• sometimes linear: P ~ PRF• often: “sweet spot”

Source: MSP430 data sheet

10

Sensor Node Energy Roadmap

2000 2002 2004

10,000

1,000

100

10

1

.1

Aver

age

Pow

er (m

W)

• Deployed (5W)

• PAC/C Baseline (.5W)

• (50 mW)

(1mW)

Rehosting to Low Power COTS(10x)

-System-On-Chip-Adv Power ManagementAlgorithms (50x)

Source: ISI & DARPA PAC/C Program

11

Communication/Computation Technology Projection

Assume: 10kbit/sec. Radio, 10 m range.

Large cost of communications relative to computation continues

1999 (Bluetooth

Technology)2004

(150nJ/bit) (5nJ/bit)1.5mW* 50uW

~ 190 MOPS(5pJ/OP)

Computation

Communication

Source: ISI & DARPA PAC/C Program

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Free Space Path Loss (Friis Law)

• Signal power decay in air:

• Proportional to the square of the distance d• Proportional to the square of the frequency f

– high frequency = high loss– low frequency = low bandwidth

13

From Week 7 slides

Ex.: Mica-Z vs. TinyNode

Mica-Z (Crossbow)•Microcontroller:

• ATmega128L• TinyOs

• Transceiver:• Chipcon CC2420• 2.4 GHz carrier• Throughput: up to 250k bps• Range: up to 75 m

TinyNode (Shockfish)• Microcontroller:

• TI MSP430• TinyOs

• Transceiver: • Semtech XE 1205• 868 and 915 MHz carriers• Throughput: up to 153k bps• Range: up to 2 km

Sensorscope data logger

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From Week 7 slides

Power Generation

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Power generation methods• Solar• Wind• Temperature difference

(Seebeck Effect)• Vibration• Hydro

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Solar power generation

1000W/m2

1000W

Efficiency:10-20%

150W

Efficiency:90%

135W

95W

Efficiency:70%

Efficiency:90%

85W

100W/m2

8,5W

100W 15W 13,5W

9,5W

0,1m2 per W (e.g., 32x32 cm2)

1m2

17

Power Storage

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Batteries• primary (non rechargeable, red)

“often a good idea” • secondary (rechargeable, blue)

• Other important parameters:• number of cycles (few 100 – few 1000) optimal conditions• cold temperature behaviour

• Interesting alternative: super capacitor• 30 Wh/kg• expensive• very high number of cycles (> 100’000)

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From a Single Instrument to a Network

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Motivation for Sensor Networks

What if we could monitor events which …

– have a large spatial and temporal distribution– require in-situ measurements– take place in hard to access places– generate data which need to be available in

real-time

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Motivation for Sensor Networks

What would we need for that?A device which …

– is cheap – so we can distribute many of it – is reliable – so we can measure for a long time– uses little power – battery/solar cell powered– has a radio – so it can communicate– can potentially move – so it can potentially

relocate

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Building a SensorNetwork: Key Concepts

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Introduction

Temperature

Humidity

Light

24

Topology

?

25

Topology

26

Topology

27

Topology

GPRS

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Topology

Pros• Very simple!• No restrictions in sensor locations

Cons• The server may be quite far from the stations• A long-range link may consume a lot of energy!

29

0

200

400

600

800

Sensor MSP430 XE1205 GPRS

Power

Consum

ption[m

A]

0.5 350

700!

14xtim

estheXE

1205

!

Topology (TinyNode Example)

30

Cur

rent

con

sum

ptio

n ov

er 4

.8 V

[mA

]

Topology (SensorScope Example)

0

200

400

600

800

Sensor MSP430 XE1205 GPRS

Power

Consum

ption[m

A]

0.5 350

700!

14xtim

estheXE

120 5

!

31

Cur

rent

con

sum

ptio

n ov

er 4

.8 V

[m

A]

Topology

Assuming four AA batteries, 1.2 V, 2000 mAh

• Sensor: 167 days• MSP430: 28 days• Short range radio: 1.7 days• Long range radio: 8 hours

32

Topology

33

But makes sense on projects such OpenSense (see next week)!

Topology

GPRS

Short range

Sink

34

TopologyRecall Friis law again:

Example: To transmit over 5 Km, using a 868 MHz carrier, we can:

• One hop of 5 km: L = 106 dB• Two hops of 2.5 km: L = 99 dB• Five hops of 1 km: L = 92 dB

Energy is the main issue !!!35

Multi-hop Sensor Network

GPRS

36

Multi-hop Sensor Network

Pros• Only one car battery in the network• The sensor network has extended monitoring coverage• Multiple routes for stations to communicate with the sink• Auto configurable network (robustness)

Cons• Significantly more complicated• Data rate is not increased• Unable to use directional antennas

37

Multi-hop WSNs

Implementation:

• Neighborhood discovery

• Data routing

• Time synchronization

• Duty-cycling (radio management)

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Prerequisite for routing

Prerequisite for duty cycling

Neighborhood

A

Hello messages (Beacons) are one common method:1. Node A sends a HELLO message to its neighbors (B, C, and D).2. Nodes B, C, and D update their neighborhood table.3. Node B sends a HELLO message to its neighbors (A, C, and D).4. …

B

D

C39

Neighborhood

A


B

D

C

(A)

(A)

(A)

40

NeighborhoodHello messages (Beacons) are one common method:1. Node A sends a HELLO message to its neighbors (B, C, and D).2. Nodes B, C, and D update their neighborhood table.3. Node B sends a HELLO message to its neighbors (A, C, and D).4. …

A

B

(B)

(A, B)

(A, B)

(A)

C

D

41

Neighborhood

(B, C, D)

(A, B, C)

(A, B, D)

A

B


D(A, C, D)

C42

Neighborhood

A

What information do we need about our neighbors?• Distance to sink• Last time heard• Link quality

C

DG

H

F

E

B

2 hops

2 hops1 hop

1 hop

1 hop

2 hops

3 hops

43

Neighborhood

A

C

DG

H

F

E

B

2 hops

2 hops1 hop

1 hop

1 hop

2 hops

3 hops

A few remarks:• Only the distance to the sink is stored.• Neighborhood discovery can’t be done only once!• We need to estimate link qualities!

Id Age Distance QualityB 2 min 1 hop 87%C 2 min 1 hop 98%F 4 min 2 hops 74%G 1 min 2 hops 93%

Node E’s neighborhood table

44

NeighborhoodVariations of simple schema:

• Each node sends X beacons per minute.• Number of beacons received per minute are stored.• Quality is estimated over the past Y minutes by counting losses.

Example (X = 10;Y = 4):

t – 4 t – 3 t - 2 t - 1

10 7 8 8 Quality = 0.8

t – 4 t – 3 t - 2 t - 1

7 8 8 6 Quality = 0.71

t – 4 t – 3 t - 2 t - 1

8 8 6 5 Quality = 0.6445

Time Synchronization

−12

−8

−4

0

4

8

Oct 17 19 21 23 25 27 29 Oct 31

Airtempe

rature

[°C]

Crystal oscillators are highly impacted by temperature!

Weather conditions, especially temperature and humidity, may have a significant effect on hardware

46


−30

−15

0

15

30

1 2 3 4 5 6 7 8

Airtempe

rature

[°C]

Indoor Outdoor Freezer

0

125

250

375

500

1 2 3 4 5 6 7 8

Timedrift

[ms]

Day 47


Nodes need to know the time to:• Timestamp packets• Synchronize actions (e.g., taking samples, transmitting data)

How do we get time:• Fully decentralized: Every node gets the time itself• Partially centralized: Time is propagated from reference nodes

48

Time SynchronizationEvery node gets the time:• Atomic clock receivers:

• Cheap (both energy and $)• Complexity• Limited coverage

• GPS:• Coverage• Complexity• High cost (energy and $)

• GPRS: same as GPS with less coverage

What about a partially centralized approach?49


A

C

DG

H

F

E

B

2 hops

2 hops1 hop

1 hop

1 hop

2 hops

3 hops

50

For instance, the sink serve as time reference node

Field Deployment of Sensor Networks

–The SensorScope Project

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Microcontrollers are inside hermitically sealed boxes, attached on a mounting pole with up to seven external sensors.

Price: 4000-5000 CHF

SensorScope

52

SensorScope

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Shockfish TinyNode with TinyOS 2.xMSP430 16-bit microcontroller @ 8MHz48KB ROM, 10KB RAM, and 512KB flash memorySemtech XE1205 radio transceiver @ 868MHz, 76Kbps

SensorScope

54

162x140mm solar panel12Ah NiMH rechargeable battery

SensorScope

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Note based on s. 17 estimation: average consumption < 220 mW for batter level maintenance!Compare with s.31 power consumption

SensorScopeMany previous successful deployments

97 stations deployed at EPFL (one year)

57

SensorScopeMany previous successful deployments

16 stations deployed at Le Génépi to monitor conditions leading to dangerous mudslides (two months)

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MicaZ – An Example of a Sensor Node and Embedded System

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MICA mote family

• designed in EECS at UCBerkeley• manufactured/marketed by Crossbow

– several thousand produced– used by several hundred research groups– about CHF 250/piece

• variety of available sensors

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MICAz

• Atmel ATmega128L– 8 bit microcontroller, ~8MHz– 128kB program memory, 4kB SRAM– 512kB external flash (data logger)

• Chipcon CC2420– 802.15.4 (Zigbee)

• 2 AA batteries– about 5 days active (15-20 mA)– about 20 years sleeping (15-20 µA)

• TinyOS61

Perception - Sensor Board

• MTS 300 CA– Light (Clairex CL94L)– Temp (Panasonic ERT-J1VR103J)– Acoustic (WM-62A Microphone)– Sounder (4 kHz Resonator)

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Computation - Operating System An operating system (OS) is an interface between

hardware and user applications. It is responsible for the management and

coordination of tasks and the sharing of the limited resources of the computer system.

A typical OS can be decomposed into the following entities: Scheduler, which is responsible for the sharing of the

processing unit (microprocessor or microcontroller) Device drivers, which are low-level programs that

manage the various devices (sensors, actuators, secondary memory storage devices, etc.).

Memory management unit, which is responsible for the sharing of the memory (virtual memory).

Optional: Graphical User Interface, File System, Security, etc.

Most “OS” for embedded systems include these two

entities only!

63

Computation - TinyOS• Minimal OS designed for Sensor Networks• Event-driven execution• Programming language: nesC (C-like syntax

but supports TinyOS concurrency model)• Widespread usage on motes

– MICA (ATmega128L)– TELOS (TI MSP430)

• Provided simulator: TosSim64

Communication - 802.15.4 / Zigbee• Emerging standard for low-power

wireless monitoring and control– 2.4 GHz ISM band (84 channels), 250

kbps data rate• Chipcon/Ember CC2420: Single-chip

transceiver– 1.8V supply

• 19.7 mA receiving• 17.4 mA transmitting

– Easy to integrate: Open source drivers– O-QPSK modulation (Code Division

Multiple Access, CDMA); “plays nice” with 802.11 and Bluetooth 65

Communication - Standards

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Conclusion

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Take Home Messages• Power is most often the key design constraint in embedded

systems• Efficient power management strategies can decrease the

consumption by several orders of magnitude• Power is comparably difficult to generate and store• Sensor networks enable environmental monitoring in remote

locations and of difficult-to-measure processes• Real-world deployments may be highly unpredictable!• The Mica-Z desktop node is the result of a pioneering open

source (both hardware and software) project at UC Berkeley; itis still widely used in educational settings and has stimulatedsignificantly the creation of the research community in wirelesssensor networks

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Additional Literature – Week 11Books• Shearer F., “Power Management in Mobile Devices”, Elsevier, 2008.

Pointers• Sensorscope: http://sensorscope.epfl.ch/• Swiss Experiment: http://www.swiss-experiment.ch/

• Permasense: http://www.permasense.ch/

• GITEWS: http://www.gitews.de• WiSARD network: http://wisardnet.nau.edu/

• CENS: http://research.cens.ucla.edu/

• TinyOS: http://www.tinyos.net/

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